Building change detection in satellite stereo imagery based on belief functions

Jiaojiao Tian, P. Reinartz, J. Dezert
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引用次数: 7

Abstract

3D Building change detection has become a popular research topic along with the improvement of image quality and computer science. When only building changes are of interest, both the multi-temporal images and Digital Surface Models provide valuable but not comprehensive information in the change detection procedure. Therefore, in this paper, belief functions have been adopted for fusing information from these two sources. In the first step, two change indicators are proposed by focusing on building changes. Both indicators have been projected to a sigmoid curve, in which both the concordance and discordance indexes are considered. In order to fuse the concordance and discordance indexes and further fuse the two change indicators, two belief functions are considered. One is the original Dempster-Shafer Theory (DST), and the most recent one is Dezert-Smarandache Theory (DSmT). This paper shows how these belief-based frameworks can help in building change detection problem. Besides using different belief functions in obtaining the global BBAs, four decision-making criteria are tested to extract final building change masks. The results have been validated by compared to the manually extracted change reference mask.
基于信念函数的卫星立体图像建筑变化检测
随着图像质量的提高和计算机技术的进步,三维建筑变化检测已成为一个热门的研究课题。当仅对建筑物变化感兴趣时,多时相图像和数字表面模型在变化检测过程中提供了有价值但不全面的信息。因此,本文采用信念函数对这两种来源的信息进行融合。在第一步中,通过关注建筑变化,提出了两个变化指标。这两个指标都被预测成一条s型曲线,其中一致性和不一致性指标都被考虑在内。为了融合一致性和不一致性指标,并进一步融合两个变化指标,考虑了两个信念函数。一个是最初的Dempster-Shafer理论(DST),一个是最近的Dezert-Smarandache理论(DSmT)。本文展示了这些基于信念的框架如何帮助构建变更检测问题。除了使用不同的信念函数来获得全局BBAs外,还测试了四种决策准则来提取最终的建筑变化掩模。通过与手动提取的变更参考掩码进行比较,验证了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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